1. Field of Invention
The present invention relates to a method, procedure, and set of computer codes for discriminating image representations of classes of objects, such as MRI signals of benign and malignant lesions, where the images have been transformed to meet constraints of the system, e.g., windowing/leveling to display a lesion in the center of a range of displayed intensities.
2. Prior Art
In many prior applications, data are represented in three dimensions where two of the dimensions (x,y) represent spatial location on a grid of fixed size and the third dimension (w) is a representation of original source data. As an example, in magnetic resonance imaging (MRI) systems, the w dimension is image intensity, which represents signal strength from the imaging system. In the MRI example, the source signal is transformed to an image intensity signal to show a medically appropriate representation of the MRI image data in a way that satisfies limitations in intensity values imposed by the display system, e.g. 28=256 discrete intensity levels. Linear transformations on the w-axis, such as windowing and leveling, are typically used to show the medically relevant portions of the image in the central portion of the displayed intensity scale. In many of these applications, the end-user could benefit from being able to distinguish and discriminate objects (e.g., lesions) within the source data on the basis of the original signal strength. However, the transformations from source signal to image intensity signal, which may vary from case to case, make this comparative analysis difficult and subject to error. In other applications data are represented in 4 dimensions, where 3 of the dimensions represent spatial location (x,y,z) and the fourth dimension (w) is a representation of the source data. All discussions and descriptions for the invention in 2 spatial dimensions are readily extended to 3 spatial dimensions. While the term pixel is frequently used to refer to 2-dimensions and the term voxel is frequently used to refer to 3-dimensions, in this application we use pixel to refer to 2-dimensions and 3-dimensions.
In the example of medical MRI, the source signals from gadolinium-enhanced images of malignant lesions are frequently stronger than the source signals from gadolinium enhanced images of benign lesions. However, after the source data have been transformed to image intensities that have been adjusted to optimize medical diagnosis, where this optimization differs from case to case, it is difficult for the radiologist to evaluate the strength of the original magnetic resonance signal on the basis of the images that are presented.
An object of the present invention is to provide a system, method and computer program product for evaluating whether a lesion in an image is cancerous, benign, or of an uncertain nature based on the intensities of the pixels in the image.
It is a further object of the present invention to provide a system, method and computer program product for evaluating a lesion which can discriminate classes of objects based on intensity and spatial relationships of pixels, e.g., gradients and shapes within or near the objects.
It is a further object of the present invention to provide a system, method and computer program product to allow a radiologist to evaluate the strength of the original image signal after the source data have been transformed to image intensities that have been adjusted to optimize medical diagnosis.
A further object is to simultaneously obtain diagnostic information from a substantial portion of a patient's body, possibly presenting multiple lesions.
Another object is to obtain highly reliable diagnostic information based on image data obtained from images corresponding to one, or at most two, time frames.
The scope and content of the present invention is not intended to be limited by or to the above mentioned objects.
The invention provides a novel method of evaluating images of a body region, wherein at least one of the images shows an abnormality indicative of a possible lesion, comprising the steps of:
determining locations of pixels in each image that show such abnormality;
for each of a set of intensity levels, I, determining a contour around the cluster containing the pixels at the locations determined in the step of determining;
defining a function, F, that discriminates a distinct characteristic of each contour in a nested sequence;
defining a function, G, used to characterize the changes in the value of the function F over the range of contours at each intensity level; and
identifying a lesion as being more likely to be benign or more likely to be cancerous based on at least one threshold value for the function G or based on threshold values and locations of pixels depicting a plurality of lesions within the body region.
Briefly, the invention is characterized in particular by the use of a method of evaluating whether a lesion in an image is cancerous, benign or uncertain, where the image includes a plurality of pixels, each pixel having a particular intensity I in the range of 0≦I<2N, where N is an integer>1. One embodiment of the method includes the steps of defining a landmark pixel within the lesion in the image, growing a cluster around the landmark pixel which contains the lesion for each intensity value in a set of possible intensity values; and at each intensity level in the set, constructing a region of interest such that the region of interest is a minimal polygon containing the cluster at that intensity level. The method further includes the steps of computing a value of a characteristic of the minimal polygon at each intensity level in the set, determining a number related to changes in the characteristic values over the range of intensity levels, and determining whether the lesion is more likely to be cancerous, benign or uncertain in response to the number related to changes in the characteristic values over the range of intensity levels.
An evaluation process according to the invention may typically start with a number of images of different body region slices. A preliminary manual or automated operation may be carried out to exclude from further processing those images that do not show evidence of a lesion.
For example, all images could be examined visually and only those images that appear to show an abnormality or abnormality could then be evaluated according to the invention.
Alternatively, all images could be evaluated according to the invention and those images for which the evaluation indicates that no lesion is present could be discarded. For example, images could be discarded if there is no evidence of an abnormality, or if evaluations of each area of interest, or abnormality, in an image generate a number of changes in the size of the image contour that is below a certain threshold value. That value could be selected on the basis of experience, but would be expected to be in the range of 3 to 5.